Statistical Software for Genetic Association Studies

用于遗传关联研究的统计软件

• 批准号: 7272149
• 负责人: ROBERTO G GUTIERREZ
• 金额: $ 10万
• 依托单位: STATACORP, LP
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7272149
• 关键词: Algorithms; Alleles; Area; Candidate Disease Gene; Case Study; Case-Control Studies; Cataloging; Catalogs; Cations; Class; Code; Companions; Complex; Computer software; Computers; Data; Dependence; Development; Diabetes Mellitus; Disease; Documentation; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiologic Studies; Epidemiologist; Equation; Equilibrium; Etiology; Future; Genes; Genetic; Genetic Markers; Genetic Models; Genotype; Goals; Hand; Haplotypes; Human Genome; Hypertension; Investigation; Journals; Knowledge; Language; Literature; Mainstreaming; Malignant Neoplasms; Manuals; Memory; Methodology; Methods; Mining; Modeling; One-Step dentin bonding system; Output; Paper; Phase; Population; Process; Publishing; Purpose; Questionnaires; Rare Diseases; Reporting; Research; Research Design; Research Personnel; Resort; Risk; Role; Running; Schizophrenia; Series; Source; Speed; Standards of Weights and Measures; Statistical Methods; Techniques; Testing; Text; Thinking; Work; Writing; authority; base; case control; desire; gene environment interaction; gene interaction; genetic analysis; genetic association; graphical user interface; model development; novel; professor; programs; prospective; prototype; research and development; success; syntax; tool; user-friendly

DESCRIPTION (provided by applicant): Risks of complex diseases, such as cancers, hypertension, diabetes, and schizophrenia, are determined by both genetic and environmental factors. Advances in human genome research have thus led to epidemiologic investigations not only of the effects of genes alone, but also of their ef- fects in combination with environmental exposures. The case-control study design, which has been widely used in classical questionnaire-based epidemiologic studies, is now commonly employed to study the role of genes and gene-environment interactions in the etiology of complex diseases. Recently, a broad class of profile-likelihood semiparametric methods has been developed for the analysis of case-control genetic data in the presence of environmental factors. These methods ex- ploit knowledge about the distribution of the basic genetic information in order to build estimators that are much more statistically efficient than other approaches, and are also statistically valid in the presence of incomplete genetic data, such as missing marker alleles and unphased haplotypes. Because this kind of methodology is not available in any commercial software, researchers have resorted to standard approaches, which lack statistical efficiency and sometimes validity. As a re- sult, important gene-environment interactions are obscured, as are important main effects. The goal of this project is to develop Stata software to implement the profile-likelihood semiparametric methods and related methods. The software will handle missing genotypes, unphased haplotypes, flexible haplotype models with haplotype-environment interactions, and models both with and with- out Hardy-Weinberg equilibrium. This tool will be highly useful to epidemiologists and geneticists in their search for genetic and environmental determinants of complex diseases. Risks of complex diseases, such as cancers, hypertension, diabetes, and schizophrenia, are deter- mined by both genetic and environmental factors. Advances in human genome research have thus led to epidemiologic investigations not only of the effects of genes alone, but also of their effects in combination with environmental exposures. This project will implement new and more ef?cient statistical methods for the analysis of case-control genetic data in the presence of environmen- tal factors, and thus bring into the mainstream better ways of detecting main genetic effects and gene-environment interactions.

描述（由申请人提供）：复杂疾病的风险，如癌症，高血压，糖尿病和精神分裂症，由遗传和环境因素决定。因此，人类基因组研究的进展不仅导致了对基因单独影响的流行病学调查，而且还导致了对基因与环境暴露相结合的影响的流行病学调查。病例-对照研究设计在经典的基于病毒的流行病学研究中被广泛使用，现在通常用于研究基因和基因-环境相互作用在复杂疾病病因学中的作用。最近，广泛的类的配置文件似然半参数方法已被开发用于分析的病例对照遗传数据中存在的环境因素。这些方法利用关于基本遗传信息的分布的知识，以便建立比其他方法统计上有效得多的估计值，并且在不完整遗传数据（例如缺失的标记等位基因和未定相的单倍型）存在下也是统计上有效的。由于这种方法在任何商业软件中都不可用，研究人员不得不求助于标准方法，这些方法缺乏统计效率，有时甚至是有效性。结果，重要的基因-环境相互作用被掩盖了，重要的主效应也被掩盖了。本计画的目标是发展Stata软体来执行剖面-似然半参数方法及相关方法。该软件将处理缺失的基因型、未定相的单倍型、具有单倍型-环境相互作用的灵活单倍型模型以及具有和不具有Hardy-Weinberg平衡的模型。这个工具将是非常有用的流行病学家和遗传学家在他们的搜索遗传和环境决定因素的复杂疾病。癌症、高血压、糖尿病和精神分裂症等复杂疾病的风险由遗传和环境因素决定。因此，人类基因组研究的进展不仅导致了对基因本身的影响的流行病学调查，而且还导致了对基因与环境暴露相结合的影响的流行病学调查。该项目将实施新的和更多的EF？在存在环境因素的情况下，对病例-对照遗传数据进行分析的有效统计方法，从而使检测主要遗传效应和基因-环境相互作用的更好方法成为主流。